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    <titleInfo>
        <title>Extracting Named Entities. A Statistical Approach</title>
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    <name type="personal">
        <namePart type="given">Joaquim</namePart>
        <namePart type="family">Silva</namePart>
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    <name type="personal">
        <namePart type="given">Zornitsa</namePart>
        <namePart type="family">Kozareva</namePart>
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    <name type="personal">
        <namePart type="given">Veska</namePart>
        <namePart type="family">Noncheva</namePart>
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    <name type="personal">
        <namePart type="given">Gabriel</namePart>
        <namePart type="family">Lopes</namePart>
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        <name type="personal">
            <namePart type="given">Philippe</namePart>
            <namePart type="family">Blache</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Noël</namePart>
            <namePart type="family">Nguyen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nouredine</namePart>
            <namePart type="family">Chenfour</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Abdenbi</namePart>
            <namePart type="family">Rajouani</namePart>
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    <abstract>Named entities and more generally Multiword Lexical Units (MWUs) are important for various applications. However, language independent methods for automatically extracting MWUs do not provide us with clean data. So, in this paper we propose a method for selecting possible named entities from automatically extracted MWUs, and later, a statistics-based language independent unsupervised approach is applied to possible named entities in order to cluster them according to their type. Statistical features used by our clustering process are described and motivated. The Model-Based Clustering Analysis (MBCA) software enabled us to obtain different clusters for proposed named entities. The method was applied to Bulgarian and English. For some clusters, precision is very high; other clusters still need further refinement. Based on the obtained clusters, it is also possible to classify new possible named entities.</abstract>
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%0 Conference Proceedings
%T Extracting Named Entities. A Statistical Approach
%A Silva, Joaquim
%A Kozareva, Zornitsa
%A Noncheva, Veska
%A Lopes, Gabriel
%Y Blache, Philippe
%Y Nguyen, Noël
%Y Chenfour, Nouredine
%Y Rajouani, Abdenbi
%S Actes de la 11ème conférence sur le Traitement Automatique des Langues Naturelles. Posters
%D 2004
%8 April
%I ATALA
%C Fès, Maroc
%F silva-etal-2004-extracting
%X Named entities and more generally Multiword Lexical Units (MWUs) are important for various applications. However, language independent methods for automatically extracting MWUs do not provide us with clean data. So, in this paper we propose a method for selecting possible named entities from automatically extracted MWUs, and later, a statistics-based language independent unsupervised approach is applied to possible named entities in order to cluster them according to their type. Statistical features used by our clustering process are described and motivated. The Model-Based Clustering Analysis (MBCA) software enabled us to obtain different clusters for proposed named entities. The method was applied to Bulgarian and English. For some clusters, precision is very high; other clusters still need further refinement. Based on the obtained clusters, it is also possible to classify new possible named entities.
%U https://aclanthology.org/2004.jeptalnrecital-poster.21/
%P 125-130

Markdown (Informal)

[Extracting Named Entities. A Statistical Approach](https://aclanthology.org/2004.jeptalnrecital-poster.21/) (Silva et al., JEP/TALN/RECITAL 2004)

• Extracting Named Entities. A Statistical Approach (Silva et al., JEP/TALN/RECITAL 2004)

ACL

• Joaquim Silva, Zornitsa Kozareva, Veska Noncheva, and Gabriel Lopes. 2004. Extracting Named Entities. A Statistical Approach. In Actes de la 11ème conférence sur le Traitement Automatique des Langues Naturelles. Posters, pages 125–130, Fès, Maroc. ATALA.